Computer Project

Consumer Research Inc.

Due Date Monday Dec. 5, 2022

p.750

1-Use methods of descriptive statistics to summarize the data. Your answer should include the followings:

a. Frequency distribution for amount charged with starting point 1800, class width 1000. For income use starting point 20 and class width of 10.

b. Histogram for Income and amount charged.

c. Crosstabulation for amount charged and income. Draw a side-by-side chart

d. Scatter diagram between amount charged and income.

e. Compute means, medians and 25^th percentile for amount charged and income.

f. Compute variance, standard deviation, coefficient of variation and correlation coefficient for amount charged and income.

g. Find five-number summaries for income and amount charged and draw a box plot for them.

Make sure each table or graph has a title and X and Y axes are correctly labeled.

Write one-page report to analyze the results. (You don't need to explain how you got those numbers.

1. Develop estimated regression equations, first using annual income as the independent variable and then using household size as the independent variable. Which variable is the better predictor of annual credit card charges? Discuss your findings.
2. Develop estimated regression equation with annual income and household size as the independent variables. Discuss your findings.
3. What is the predicted annual credit card charge for a two-person household with an annual income of $50,000?
4. Discuss the need for other independent variables that could be added to the model. What additional variables might be helpful?

• This is a group project (group of 2). Group members are randomly selected by canvas.
• Data file will be posted on Canvas. For ease of computations the number of observations is reduced to 40 instead of 50.
• You need to use EXCEL to draw graphs and tables and run regression (Install Data Analysis)
• You can start working on part 1 but wait until I cover regression analysis in the next 2 weeks, then answer the rest of the questions.

For parts 2 &3 after you run the regression:

• First interpret the coefficients.
• Run a t-test for the coefficients (Critical value and p-value approach).
• Interpret R Square, Standard error.
• Construct a confidence interval for the coefficients.